diff --git "a/modeling_jamba.py" "b/modeling_jamba.py"
new file mode 100644--- /dev/null
+++ "b/modeling_jamba.py"
@@ -0,0 +1,2122 @@
+# coding=utf-8
+# Copyright 2024 AI21 Labs Ltd. and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" PyTorch Jamba model."""
+import inspect
+import math
+import warnings
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import (
+ _prepare_4d_causal_attention_mask,
+ _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from transformers.modeling_outputs import (
+ MoeCausalLMOutputWithPast,
+ MoeModelOutputWithPast,
+ SequenceClassifierOutputWithPast,
+)
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_13
+from transformers.utils import (
+ add_start_docstrings,
+ add_start_docstrings_to_model_forward,
+ is_flash_attn_greater_or_equal_2_10,
+ logging,
+ replace_return_docstrings,
+)
+from transformers.utils.import_utils import is_torch_fx_available
+from .configuration_jamba import JambaConfig
+
+
+# try except block so it'll work with trust_remote_code. Later we can have `if is_flash_attn_2_available():`
+try:
+ from flash_attn import flash_attn_func, flash_attn_varlen_func
+ from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input # noqa
+
+ _flash_supports_window_size = "window_size" in list(inspect.signature(flash_attn_func).parameters)
+except ImportError:
+ pass
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+ if not is_torch_greater_or_equal_than_1_13:
+ import torch.fx
+
+ _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+# try except block so it'll work with trust_remote_code. Later we can have `if is_mamba_ssm_available():`
+try:
+ from mamba_ssm.ops.selective_scan_interface import mamba_inner_fn, selective_scan_fn
+ from mamba_ssm.ops.triton.selective_state_update import selective_state_update
+except ImportError:
+ selective_state_update, selective_scan_fn, mamba_inner_fn = None, None, None
+
+# try except block so it'll work with trust_remote_code. Later we can have `if is_causal_conv1d_available():`
+try:
+ from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
+except ImportError:
+ causal_conv1d_update, causal_conv1d_fn = None, None
+
+is_fast_path_available = all(
+ (selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)
+)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "JambaConfig"
+
+
+# Adapted from transformers.models.mixtral.modeling_mixtral.load_balancing_loss_func
+def load_balancing_loss_func(
+ gate_logits: torch.Tensor, num_experts: torch.Tensor = None, top_k=2, attention_mask: Optional[torch.Tensor] = None
+) -> float:
+ r"""
+ Computes auxiliary load balancing loss as in Switch Transformer - implemented in Pytorch.
+
+ See Switch Transformer (https://arxiv.org/abs/2101.03961) for more details. This function implements the loss
+ function presented in equations (4) - (6) of the paper. It aims at penalizing cases where the routing between
+ experts is too unbalanced.
+
+ Args:
+ gate_logits (Union[`torch.Tensor`, Tuple[torch.Tensor]):
+ Logits from the `router`, should be a tuple of model.config.num_hidden_layers tensors of
+ shape [batch_size X sequence_length, num_experts].
+ attention_mask (`torch.Tensor`, None):
+ The attention_mask used in forward function
+ shape [batch_size X sequence_length] if not None.
+ num_experts (`int`, *optional*):
+ Number of experts
+
+ Returns:
+ The auxiliary loss.
+ """
+ if gate_logits is None or not isinstance(gate_logits, tuple):
+ return 0
+
+ if isinstance(gate_logits, tuple):
+ compute_device = gate_logits[0].device
+ concatenated_gate_logits = torch.cat(
+ [layer_gate.to(compute_device) for layer_gate in gate_logits if layer_gate.shape[1] > 1], dim=0
+ )
+
+ routing_weights = torch.nn.functional.softmax(concatenated_gate_logits, dim=-1)
+
+ _, selected_experts = torch.topk(routing_weights, top_k, dim=-1)
+
+ expert_mask = torch.nn.functional.one_hot(selected_experts, num_experts)
+
+ if attention_mask is None:
+ # Compute the percentage of tokens routed to each experts
+ tokens_per_expert = torch.mean(expert_mask.float(), dim=0)
+
+ # Compute the average probability of routing to these experts
+ router_prob_per_expert = torch.mean(routing_weights, dim=0)
+ else:
+ batch_size, sequence_length = attention_mask.shape
+ num_hidden_layers = concatenated_gate_logits.shape[0] // (batch_size * sequence_length)
+
+ # Compute the mask that masks all padding tokens as 0 with the same shape of expert_mask
+ expert_attention_mask = (
+ attention_mask[None, :, :, None, None]
+ .expand((num_hidden_layers, batch_size, sequence_length, top_k, num_experts))
+ .reshape(-1, top_k, num_experts)
+ .to(compute_device)
+ )
+
+ # Compute the percentage of tokens routed to each experts
+ tokens_per_expert = torch.sum(expert_mask.float() * expert_attention_mask, dim=0) / torch.sum(
+ expert_attention_mask, dim=0
+ )
+
+ # Compute the mask that masks all padding tokens as 0 with the same shape of tokens_per_expert
+ router_per_expert_attention_mask = (
+ attention_mask[None, :, :, None]
+ .expand((num_hidden_layers, batch_size, sequence_length, num_experts))
+ .reshape(-1, num_experts)
+ .to(compute_device)
+ )
+
+ # Compute the average probability of routing to these experts
+ router_prob_per_expert = torch.sum(routing_weights * router_per_expert_attention_mask, dim=0) / torch.sum(
+ router_per_expert_attention_mask, dim=0
+ )
+
+ overall_loss = torch.sum(tokens_per_expert * router_prob_per_expert.unsqueeze(0))
+ return overall_loss * num_experts
+
+
+# Copied from transformers.models.llama.modeling_llama._get_unpad_data
+def _get_unpad_data(attention_mask):
+ seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+ indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+ max_seqlen_in_batch = seqlens_in_batch.max().item()
+ cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.int32), (1, 0))
+ return (
+ indices,
+ cu_seqlens,
+ max_seqlen_in_batch,
+ )
+
+
+# Copied from transformers.models.llama.modeling_llama.LlamaRMSNorm with Llama->Jamba
+class JambaRMSNorm(nn.Module):
+ def __init__(self, hidden_size, eps=1e-6):
+ """
+ JambaRMSNorm is equivalent to T5LayerNorm
+ """
+ super().__init__()
+ self.weight = nn.Parameter(torch.ones(hidden_size))
+ self.variance_epsilon = eps
+
+ def forward(self, hidden_states):
+ input_dtype = hidden_states.dtype
+ hidden_states = hidden_states.to(torch.float32)
+ variance = hidden_states.pow(2).mean(-1, keepdim=True)
+ hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
+ return self.weight * hidden_states.to(input_dtype)
+
+
+# Copied from transformers.models.llama.modeling_llama.repeat_kv
+def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """
+ This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch,
+ num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim)
+ """
+ batch, num_key_value_heads, slen, head_dim = hidden_states.shape
+ if n_rep == 1:
+ return hidden_states
+ hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim)
+ return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim)
+
+
+# Adapted from transformers.models.mistral.modeling_mistral.MistralAttention with Mistral->Jamba
+class JambaAttention(nn.Module):
+ """
+ Multi-headed attention from 'Attention Is All You Need' paper. Modified to use sliding window attention: Longformer
+ and "Generating Long Sequences with Sparse Transformers".
+ """
+
+ def __init__(self, config: JambaConfig, layer_idx: Optional[int] = None):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ if layer_idx is None:
+ logger.warning_once(
+ f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will "
+ "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` "
+ "when creating this class."
+ )
+
+ self.hidden_size = config.hidden_size
+ self.num_heads = config.num_attention_heads
+ self.head_dim = self.hidden_size // self.num_heads
+ self.num_key_value_heads = config.num_key_value_heads
+ self.num_key_value_groups = self.num_heads // self.num_key_value_heads
+ self.is_causal = True
+ self.attention_dropout = config.attention_dropout
+
+ if (self.head_dim * self.num_heads) != self.hidden_size:
+ raise ValueError(
+ f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+ f" and `num_heads`: {self.num_heads})."
+ )
+ self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=False)
+ self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+ self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)
+ self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=False)
+
+ def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+ return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ if self.layer_idx is None:
+ raise ValueError(
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+ "with a layer index."
+ )
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+ if past_key_value is not None:
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx)
+
+ # repeat k/v heads if n_kv_heads < n_heads
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
+
+ if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+ f" {attn_weights.size()}"
+ )
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+
+ attn_weights = attn_weights + attention_mask
+
+ # upcast attention to fp32
+ attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
+ attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training)
+ attn_output = torch.matmul(attn_weights, value_states)
+
+ if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
+ raise ValueError(
+ f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
+ f" {attn_output.size()}"
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value
+
+
+# Adapted from transformers.models.mistral.modeling_mistral.MistralFlashAttention2 with Mistral->Jamba
+class JambaFlashAttention2(JambaAttention):
+ """
+ Jamba flash attention module. This module inherits from `JambaAttention` as the weights of the module stays
+ untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+ flash attention and deal with padding tokens in case the input contains any of them.
+ """
+
+ # Copied from transformers.models.llama.modeling_llama.LlamaFlashAttention2.__init__
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+
+ # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+ # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+ # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+ self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ **kwargs,
+ ):
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+
+ # overwrite attention_mask with padding_mask
+ attention_mask = kwargs.pop("padding_mask")
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ if self.layer_idx is None:
+ raise ValueError(
+ f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+ "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+ "with a layer index."
+ )
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+ use_sliding_windows = (
+ _flash_supports_window_size
+ and getattr(self.config, "sliding_window", None) is not None
+ and kv_seq_len > self.config.sliding_window
+ )
+
+ if not _flash_supports_window_size:
+ logger.warning_once(
+ "The current flash attention version does not support sliding window attention, for a more memory efficient implementation"
+ " make sure to upgrade flash-attn library."
+ )
+
+ if past_key_value is not None:
+ # Activate slicing cache only if the config has a value `sliding_windows` attribute
+ cache_has_contents = past_key_value.get_seq_length(self.layer_idx) > 0
+ if (
+ getattr(self.config, "sliding_window", None) is not None
+ and kv_seq_len > self.config.sliding_window
+ and cache_has_contents
+ ):
+ slicing_tokens = 1 - self.config.sliding_window
+
+ past_key = past_key_value[self.layer_idx][0]
+ past_value = past_key_value[self.layer_idx][1]
+
+ past_key = past_key[:, :, slicing_tokens:, :].contiguous()
+ past_value = past_value[:, :, slicing_tokens:, :].contiguous()
+
+ if past_key.shape[-2] != self.config.sliding_window - 1:
+ raise ValueError(
+ f"past key must have a shape of (`batch_size, num_heads, self.config.sliding_window-1, head_dim`), got"
+ f" {past_key.shape}"
+ )
+
+ if attention_mask is not None:
+ attention_mask = attention_mask[:, slicing_tokens:]
+ attention_mask = torch.cat([attention_mask, torch.ones_like(attention_mask[:, -1:])], dim=-1)
+
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx)
+
+ # repeat k/v heads if n_kv_heads < n_heads
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+ dropout_rate = 0.0 if not self.training else self.attention_dropout
+
+ # In PEFT, usually we cast the layer norms in float32 for training stability reasons
+ # therefore the input hidden states gets silently casted in float32. Hence, we need
+ # cast them back in float16 just to be sure everything works as expected.
+ input_dtype = query_states.dtype
+ if input_dtype == torch.float32:
+ if torch.is_autocast_enabled():
+ target_dtype = torch.get_autocast_gpu_dtype()
+ # Handle the case where the model is quantized
+ elif hasattr(self.config, "_pre_quantization_dtype"):
+ target_dtype = self.config._pre_quantization_dtype
+ else:
+ target_dtype = self.q_proj.weight.dtype
+
+ logger.warning_once(
+ f"The input hidden states seems to be silently casted in float32, this might be related to"
+ f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in"
+ f" {target_dtype}."
+ )
+
+ query_states = query_states.to(target_dtype)
+ key_states = key_states.to(target_dtype)
+ value_states = value_states.to(target_dtype)
+
+ # Reashape to the expected shape for Flash Attention
+ query_states = query_states.transpose(1, 2)
+ key_states = key_states.transpose(1, 2)
+ value_states = value_states.transpose(1, 2)
+
+ attn_output = self._flash_attention_forward(
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ q_len,
+ dropout=dropout_rate,
+ use_sliding_windows=use_sliding_windows,
+ )
+
+ attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+ attn_output = self.o_proj(attn_output)
+
+ if not output_attentions:
+ attn_weights = None
+
+ return attn_output, attn_weights, past_key_value
+
+ def _flash_attention_forward(
+ self,
+ query_states,
+ key_states,
+ value_states,
+ attention_mask,
+ query_length,
+ dropout=0.0,
+ softmax_scale=None,
+ use_sliding_windows=False,
+ ):
+ """
+ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+ first unpad the input, then computes the attention scores and pad the final attention scores.
+
+ Args:
+ query_states (`torch.Tensor`):
+ Input query states to be passed to Flash Attention API
+ key_states (`torch.Tensor`):
+ Input key states to be passed to Flash Attention API
+ value_states (`torch.Tensor`):
+ Input value states to be passed to Flash Attention API
+ attention_mask (`torch.Tensor`):
+ The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+ position of padding tokens and 1 for the position of non-padding tokens.
+ dropout (`int`, *optional*):
+ Attention dropout
+ softmax_scale (`float`, *optional*):
+ The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+ use_sliding_windows (`bool`, *optional*):
+ Whether to activate sliding window attention.
+ """
+ if not self._flash_attn_uses_top_left_mask:
+ causal = self.is_causal
+ else:
+ # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in LlamaFlashAttention2 __init__.
+ causal = self.is_causal and query_length != 1
+
+ # Contains at least one padding token in the sequence
+ if attention_mask is not None:
+ batch_size = query_states.shape[0]
+ query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+ query_states, key_states, value_states, attention_mask, query_length
+ )
+
+ cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+ max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+
+ if not use_sliding_windows:
+ attn_output_unpad = flash_attn_varlen_func(
+ query_states,
+ key_states,
+ value_states,
+ cu_seqlens_q=cu_seqlens_q,
+ cu_seqlens_k=cu_seqlens_k,
+ max_seqlen_q=max_seqlen_in_batch_q,
+ max_seqlen_k=max_seqlen_in_batch_k,
+ dropout_p=dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ )
+ else:
+ attn_output_unpad = flash_attn_varlen_func(
+ query_states,
+ key_states,
+ value_states,
+ cu_seqlens_q=cu_seqlens_q,
+ cu_seqlens_k=cu_seqlens_k,
+ max_seqlen_q=max_seqlen_in_batch_q,
+ max_seqlen_k=max_seqlen_in_batch_k,
+ dropout_p=dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ window_size=(self.config.sliding_window, self.config.sliding_window),
+ )
+
+ attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+ else:
+ if not use_sliding_windows:
+ attn_output = flash_attn_func(
+ query_states,
+ key_states,
+ value_states,
+ dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ )
+ else:
+ attn_output = flash_attn_func(
+ query_states,
+ key_states,
+ value_states,
+ dropout,
+ softmax_scale=softmax_scale,
+ causal=causal,
+ window_size=(self.config.sliding_window, self.config.sliding_window),
+ )
+
+ return attn_output
+
+ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+ batch_size, kv_seq_len, num_heads, head_dim = key_layer.shape
+
+ # On the first iteration we need to properly re-create the padding mask
+ # by slicing it on the proper place
+ if kv_seq_len != attention_mask.shape[-1]:
+ attention_mask_num_tokens = attention_mask.shape[-1]
+ attention_mask = attention_mask[:, attention_mask_num_tokens - kv_seq_len :]
+
+ indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+
+ key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+ value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k)
+
+ if query_length == kv_seq_len:
+ query_layer = index_first_axis(
+ query_layer.reshape(batch_size * kv_seq_len, num_heads, head_dim), indices_k
+ )
+ cu_seqlens_q = cu_seqlens_k
+ max_seqlen_in_batch_q = max_seqlen_in_batch_k
+ indices_q = indices_k
+ elif query_length == 1:
+ max_seqlen_in_batch_q = 1
+ cu_seqlens_q = torch.arange(
+ batch_size + 1, dtype=torch.int32, device=query_layer.device
+ ) # There is a memcpy here, that is very bad.
+ indices_q = cu_seqlens_q[:-1]
+ query_layer = query_layer.squeeze(1)
+ else:
+ # The -q_len: slice assumes left padding.
+ attention_mask = attention_mask[:, -query_length:]
+ query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+ return (
+ query_layer,
+ key_layer,
+ value_layer,
+ indices_q,
+ (cu_seqlens_q, cu_seqlens_k),
+ (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+ )
+
+
+# Adapted from transformers.models.mistral.modeling_mistral.MistralSdpaAttention with Mistral->Jamba
+class JambaSdpaAttention(JambaAttention):
+ """
+ Jamba attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+ `JambaAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+ SDPA API.
+ """
+
+ # Adapted from JambaAttention.forward
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Cache] = None,
+ output_attentions: bool = False,
+ use_cache: bool = False,
+ ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+ if output_attentions:
+ # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+ logger.warning_once(
+ "JambaModel is using JambaSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, "
+ 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
+ )
+ return super().forward(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+
+ bsz, q_len, _ = hidden_states.size()
+
+ query_states = self.q_proj(hidden_states)
+ key_states = self.k_proj(hidden_states)
+ value_states = self.v_proj(hidden_states)
+
+ query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
+ key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+ value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2)
+
+ kv_seq_len = key_states.shape[-2]
+ if past_key_value is not None:
+ kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+
+ if past_key_value is not None:
+ key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx)
+
+ key_states = repeat_kv(key_states, self.num_key_value_groups)
+ value_states = repeat_kv(value_states, self.num_key_value_groups)
+
+ if attention_mask is not None:
+ if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+ raise ValueError(
+ f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+ )
+
+ # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask,
+ # Reference: https://github.com/pytorch/pytorch/issues/112577.
+ if query_states.device.type == "cuda" and attention_mask is not None:
+ query_states = query_states.contiguous()
+ key_states = key_states.contiguous()
+ value_states = value_states.contiguous()
+
+ attn_output = torch.nn.functional.scaled_dot_product_attention(
+ query_states,
+ key_states,
+ value_states,
+ attn_mask=attention_mask,
+ dropout_p=self.attention_dropout if self.training else 0.0,
+ # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+ is_causal=self.is_causal and attention_mask is None and q_len > 1,
+ )
+
+ attn_output = attn_output.transpose(1, 2).contiguous()
+ attn_output = attn_output.view(bsz, q_len, self.hidden_size)
+
+ attn_output = self.o_proj(attn_output)
+
+ return attn_output, None, past_key_value
+
+
+JAMBA_ATTENTION_CLASSES = {
+ "eager": JambaAttention,
+ "flash_attention_2": JambaFlashAttention2,
+ "sdpa": JambaSdpaAttention,
+}
+
+
+class HybridMambaAttentionDynamicCache(DynamicCache):
+ """
+ A dynamic cache that can handle both the attention cache (which has a seq_len dimension) and the mamba cache
+ (which has a constant shape regardless of seq_len).
+
+ It stores the Key and Value states as a list of tensors, one for each layer.
+ The expected shape for each tensor for attention layers is `[batch_size, num_heads, seq_len, head_dim]`.
+ For the mamba layers, the `key_cache` represents the convolution state and has a shape of `[batch_size, d_inner, 1, d_conv]`,
+ and the `value_cache` represents the ssm state and has a shape of `[batch_size, d_inner, 1, d_state]`. Mamba cache
+ shape[2] is a dummy "seqlen" dimension to match the number of attention cache dimensions. For mamba, the cache
+ doesn't grow with seqlen so this dimension is always 1.
+ """
+
+ def __init__(self) -> None:
+ super().__init__()
+ self.attention_layer_idx = None # used to know which layer has data on seqlen in the cache shape
+
+ def update(
+ self,
+ key_states: torch.Tensor,
+ value_states: torch.Tensor,
+ layer_idx: int,
+ cache_kwargs: Optional[Dict[str, Any]] = None,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """
+ Updates the cache with the new `key_states` and `value_states` for the layer `layer_idx`.
+
+ Parameters:
+ key_states (`torch.Tensor`):
+ The new key states to cache.
+ value_states (`torch.Tensor`):
+ The new value states to cache.
+ layer_idx (`int`):
+ The index of the layer to cache the states for.
+ cache_kwargs (`Dict[str, Any]`, `optional`):
+ Additional arguments for the cache subclass. No additional arguments are used in `HybridMambaAttentionDynamicCache`.
+
+ Return:
+ A tuple containing the updated key and value states.
+ """
+ # Update the number of seen tokens
+ if self.attention_layer_idx is None and self._is_attn_layer(key_states, value_states):
+ self.attention_layer_idx = layer_idx
+ if self.attention_layer_idx is not None and layer_idx == self.attention_layer_idx:
+ if hasattr(self, "_seen_tokens"):
+ self._seen_tokens += key_states.shape[-2]
+ else:
+ self.seen_tokens += key_states.shape[-2]
+
+ # Update the cache
+ if len(self.key_cache) <= layer_idx:
+ self.key_cache.append(key_states)
+ self.value_cache.append(value_states)
+ else:
+ if self._is_attn_layer(self.key_cache[layer_idx], self.value_cache[layer_idx]):
+ # attention layer - append the new states to the existing cache on the seqlen dimension
+ self.key_cache[layer_idx] = torch.cat([self.key_cache[layer_idx], key_states], dim=-2)
+ self.value_cache[layer_idx] = torch.cat([self.value_cache[layer_idx], value_states], dim=-2)
+ else:
+ # mamba layer - replace the cache with the new states
+ self.key_cache[layer_idx] = key_states
+ self.value_cache[layer_idx] = value_states
+
+ return self.key_cache[layer_idx], self.value_cache[layer_idx]
+
+ def get_seq_length(self, layer_idx: Optional[int] = None) -> int:
+ """Returns the sequence length of the cached states. A layer index can be optionally passed."""
+ if layer_idx is not None:
+ if len(self.key_cache) <= layer_idx:
+ return 0
+ if self._is_attn_layer(self.key_cache[layer_idx], self.value_cache[layer_idx]):
+ return self.key_cache[layer_idx].shape[-2]
+ else:
+ warnings.warn(
+ f"Asked to get the sequence length from cache of layer {layer_idx} which is not an attention layer. "
+ f"Ignoring that and using an attention layer cache"
+ )
+ if self.attention_layer_idx is None or len(self.key_cache) <= self.attention_layer_idx:
+ return 0
+ return self.key_cache[self.attention_layer_idx].shape[-2]
+
+ @staticmethod
+ def _is_attn_layer(key_states: torch.Tensor, value_states: torch.Tensor):
+ return key_states.shape[-1] == value_states.shape[-1]
+
+
+@dataclass
+class MambaCacheParams:
+ seqlen_offset: int = 0
+ conv_states: Dict[int, torch.Tensor] = field(default_factory=dict)
+ ssm_states: Dict[int, torch.Tensor] = field(default_factory=dict)
+
+
+# Adapted from transformers.models.mamba.modeling_mamba.MambaMixer
+class JambaMambaMixer(nn.Module):
+ """
+ Compute ∆, A, B, C, and D the state space parameters and compute the `contextualized_states`.
+ A, D are input independent (see Mamba paper [1] Section 3.5.2 "Interpretation of A" for why A isn't selective)
+ ∆, B, C are input-dependent (this is a key difference between Mamba and the linear time invariant S4,
+ and is why Mamba is called **selective** state spaces)
+ """
+
+ def __init__(self, config: JambaConfig, layer_idx):
+ super().__init__()
+ self.config = config
+ self.layer_idx = layer_idx
+ self.hidden_size = config.hidden_size
+ self.ssm_state_size = config.mamba_d_state
+ self.conv_kernel_size = config.mamba_d_conv
+ self.intermediate_size = config.mamba_expand * config.hidden_size
+ self.time_step_rank = config.mamba_dt_rank
+ self.use_conv_bias = config.mamba_conv_bias
+ self.use_bias = config.mamba_proj_bias
+ self.conv1d = nn.Conv1d(
+ in_channels=self.intermediate_size,
+ out_channels=self.intermediate_size,
+ bias=self.use_conv_bias,
+ kernel_size=self.conv_kernel_size,
+ groups=self.intermediate_size,
+ padding=self.conv_kernel_size - 1,
+ )
+
+ self.activation = config.hidden_act
+ self.act = ACT2FN[config.hidden_act]
+ self.apply_inner_layernorms = config.mamba_inner_layernorms
+
+ self.use_fast_kernels = config.use_mamba_kernels
+
+ # projection of the input hidden states
+ self.in_proj = nn.Linear(self.hidden_size, self.intermediate_size * 2, bias=self.use_bias)
+ # selective projection used to make dt, B and C input dependant
+ self.x_proj = nn.Linear(self.intermediate_size, self.time_step_rank + self.ssm_state_size * 2, bias=False)
+ # time step projection (discretization)
+ self.dt_proj = nn.Linear(self.time_step_rank, self.intermediate_size, bias=True)
+
+ # S4D real initialization. These are not discretized!
+ # The core is to load them, compute the discrete states, then write the updated state. Keeps the memory bounded
+ A = torch.arange(1, self.ssm_state_size + 1, dtype=torch.float32)[None, :]
+ A = A.expand(self.intermediate_size, -1).contiguous()
+
+ self.A_log = nn.Parameter(torch.log(A))
+ self.D = nn.Parameter(torch.ones(self.intermediate_size))
+ self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=self.use_bias)
+
+ if self.apply_inner_layernorms:
+ self.dt_layernorm = JambaRMSNorm(self.time_step_rank, eps=config.rms_norm_eps)
+ self.B_layernorm = JambaRMSNorm(self.ssm_state_size, eps=config.rms_norm_eps)
+ self.C_layernorm = JambaRMSNorm(self.ssm_state_size, eps=config.rms_norm_eps)
+ else:
+ self.dt_layernorm = None
+ self.B_layernorm = None
+ self.C_layernorm = None
+
+ if not is_fast_path_available:
+ logger.warning_once(
+ "The fast path is not available because on of `(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)`"
+ " is None. To install follow https://github.com/state-spaces/mamba/#installation and"
+ " https://github.com/Dao-AILab/causal-conv1d. If you want to use the naive implementation, set `use_mamba_kernels=False` in the model config"
+ )
+
+ def _apply_layernorms(self, dt, B, C):
+ if self.dt_layernorm is not None:
+ dt = self.dt_layernorm(dt)
+ if self.B_layernorm is not None:
+ B = self.B_layernorm(B)
+ if self.C_layernorm is not None:
+ C = self.C_layernorm(C)
+ return dt, B, C
+
+ def cuda_kernels_forward(self, hidden_states: torch.Tensor, cache_params: MambaCacheParams = None):
+ # 1. Gated MLP's linear projection
+ projected_states = self.in_proj(hidden_states).transpose(1, 2)
+
+ if (
+ self.training and cache_params is None and not self.apply_inner_layernorms
+ ): # Doesn't support outputting the states -> used for training
+ contextualized_states = mamba_inner_fn(
+ projected_states,
+ self.conv1d.weight,
+ self.conv1d.bias if self.use_conv_bias else None,
+ self.x_proj.weight,
+ self.dt_proj.weight,
+ self.out_proj.weight,
+ self.out_proj.bias.float() if self.use_bias else None,
+ -torch.exp(self.A_log.float()),
+ None, # input-dependent B
+ None, # input-dependent C
+ self.D.float(),
+ delta_bias=self.dt_proj.bias.float(),
+ delta_softplus=True,
+ )
+
+ else:
+ hidden_states, gate = projected_states.chunk(2, dim=1)
+
+ # 2. Convolution sequence transformation
+ conv_weights = self.conv1d.weight.view(self.conv1d.weight.size(0), self.conv1d.weight.size(2))
+ if cache_params is not None and cache_params.seqlen_offset > 0:
+ hidden_states = causal_conv1d_update(
+ hidden_states.squeeze(-1),
+ cache_params.conv_states[self.layer_idx],
+ conv_weights,
+ self.conv1d.bias,
+ self.activation,
+ )
+ hidden_states = hidden_states.unsqueeze(-1)
+ else:
+ if cache_params is not None:
+ conv_states = nn.functional.pad(
+ hidden_states, (self.conv_kernel_size - hidden_states.shape[-1], 0)
+ )
+ cache_params.conv_states[self.layer_idx].copy_(conv_states)
+ hidden_states = causal_conv1d_fn(
+ hidden_states, conv_weights, self.conv1d.bias, activation=self.activation
+ )
+
+ # 3. State Space Model sequence transformation
+ # 3.a. input varying initialization of time_step, B and C
+ ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
+ time_step, B, C = torch.split(
+ ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1
+ )
+ time_step, B, C = self._apply_layernorms(time_step, B, C)
+
+ # Here we need to apply dt_proj without the bias, as the bias is added in the selective scan kernel.
+ # This is a hack to apply dt_proj while still using the forward pass of `torch.nn.Linear`, which is needed
+ # in order to make quantization work. Quantization code replaces `torch.nn.Linear` layers with quantized
+ # linear layers, and requires to call the forward pass directly.
+ # The original code here was: ```discrete_time_step = self.dt_proj.weight @ time_step.transpose(1, 2)```
+ if hasattr(self.dt_proj, "base_layer"):
+ # In case of LoRA, we need to access the base layer to get the weight
+ time_proj_bias = self.dt_proj.base_layer.bias
+ self.dt_proj.base_layer.bias = None
+ else:
+ time_proj_bias = self.dt_proj.bias
+ self.dt_proj.bias = None
+ discrete_time_step = self.dt_proj(time_step).transpose(1, 2)
+ if hasattr(self.dt_proj, "base_layer"):
+ self.dt_proj.base_layer.bias = time_proj_bias
+ else:
+ self.dt_proj.bias = time_proj_bias
+
+ A = -torch.exp(self.A_log.float())
+ # 3.c perform the recurrence y ← SSM(A, B, C)(x)
+ time_proj_bias = time_proj_bias.float() if time_proj_bias is not None else None
+ if cache_params is not None and cache_params.seqlen_offset > 0:
+ scan_outputs = selective_state_update(
+ cache_params.ssm_states[self.layer_idx],
+ hidden_states[..., 0],
+ discrete_time_step[..., 0],
+ A,
+ B[:, 0],
+ C[:, 0],
+ self.D,
+ gate[..., 0],
+ time_proj_bias,
+ dt_softplus=True,
+ ).unsqueeze(-1)
+ else:
+ scan_outputs, ssm_state = selective_scan_fn(
+ hidden_states,
+ discrete_time_step,
+ A,
+ B.transpose(1, 2),
+ C.transpose(1, 2),
+ self.D.float(),
+ gate,
+ time_proj_bias,
+ delta_softplus=True,
+ return_last_state=True,
+ )
+ if ssm_state is not None and cache_params is not None:
+ cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+
+ # 4. Final linear projection
+ contextualized_states = self.out_proj(scan_outputs.transpose(1, 2))
+ return contextualized_states
+
+ # fmt: off
+ def slow_forward(self, input_states, cache_params: MambaCacheParams = None):
+ batch_size, seq_len, _ = input_states.shape
+ dtype = input_states.dtype
+ # 1. Gated MLP's linear projection
+ projected_states = self.in_proj(input_states).transpose(1, 2) # [batch, 2 * intermediate_size, seq_len]
+ hidden_states, gate = projected_states.chunk(2, dim=1)
+
+ # 2. Convolution sequence transformation
+ if cache_params is not None:
+ if self.training:
+ # In training mode, we don't want to perform in-place operations on ssm_state so we can compute the backwards pass
+ ssm_state = cache_params.ssm_states[self.layer_idx].clone()
+ else:
+ ssm_state = cache_params.ssm_states[self.layer_idx]
+
+ if cache_params.seqlen_offset > 0:
+ conv_state = cache_params.conv_states[self.layer_idx] # [batch, intermediate_size, conv_kernel_size]
+ conv_state = torch.roll(conv_state, shifts=-1, dims=-1)
+ conv_state[:, :, -1] = hidden_states[:, :, 0]
+ cache_params.conv_states[self.layer_idx].copy_(conv_state)
+ hidden_states = torch.sum(conv_state * self.conv1d.weight[:, 0, :], dim=-1)
+ if self.use_conv_bias:
+ hidden_states += self.conv1d.bias
+ hidden_states = self.act(hidden_states).to(dtype).unsqueeze(-1) # [batch, intermediate_size, 1] : decoding
+ else:
+ conv_state = nn.functional.pad(
+ hidden_states,
+ (self.conv_kernel_size - hidden_states.shape[-1], 0)
+ )
+ cache_params.conv_states[self.layer_idx].copy_(conv_state)
+ hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) # [batch, intermediate_size, seq_len]
+ else:
+ ssm_state = torch.zeros(
+ (batch_size, self.intermediate_size, self.ssm_state_size),
+ device=hidden_states.device, dtype=dtype
+ )
+ hidden_states = self.act(self.conv1d(hidden_states)[..., :seq_len]) # [batch, intermediate_size, seq_len]
+
+ # 3. State Space Model sequence transformation
+ # 3.a. Selection: [batch, seq_len, self.time_step_rank + self.ssm_state_size * 2]
+ ssm_parameters = self.x_proj(hidden_states.transpose(1, 2))
+ time_step, B, C = torch.split(
+ ssm_parameters, [self.time_step_rank, self.ssm_state_size, self.ssm_state_size], dim=-1
+ )
+ time_step, B, C = self._apply_layernorms(time_step, B, C)
+ discrete_time_step = self.dt_proj(time_step) # [batch, seq_len, intermediate_size]
+ discrete_time_step = nn.functional.softplus(discrete_time_step).transpose(1, 2) # [batch, intermediate_size, seq_len]
+
+ # 3.b. Discretization: B and C to [batch, seq_len, intermediate_size, ssm_state_size] (SRAM)
+ A = -torch.exp(self.A_log.float()) # [intermediate_size, ssm_state_size]
+ discrete_A = torch.exp(A[None, :, None, :] * discrete_time_step[:, :, :, None]) # [batch, intermediate_size, seq_len, ssm_state_size]
+ discrete_B = discrete_time_step[:, :, :, None] * B[:, None, :, :].float() # [batch, intermediade_size, seq_len, ssm_state_size]
+ deltaB_u = discrete_B * hidden_states[:, :, :, None].float()
+
+ # 3.c perform the recurrence y ← SSM(A, B, C)(x)
+ scan_outputs = []
+ for i in range(seq_len):
+ ssm_state = discrete_A[:, :, i, :] * ssm_state + deltaB_u[:, :, i, :] # [batch, intermediade_size, ssm_state]
+ scan_output = torch.matmul(ssm_state.to(dtype), C[:, i, :].unsqueeze(-1)) # [batch, intermediade_size, 1]
+ scan_outputs.append(scan_output[:, :, 0])
+ scan_output = torch.stack(scan_outputs, dim=-1) # [batch, intermediade_size, seq_len]
+ scan_output = scan_output + (hidden_states * self.D[None, :, None])
+ scan_output = (scan_output * self.act(gate))
+
+ if cache_params is not None:
+ cache_params.ssm_states[self.layer_idx].copy_(ssm_state)
+
+ # 4. Final linear projection
+ contextualized_states = self.out_proj(scan_output.transpose(1, 2)) # [batch, seq_len, hidden_size]
+ return contextualized_states
+ # fmt: on
+
+ def mixer_forward(self, hidden_states, cache_params: MambaCacheParams = None):
+ if self.use_fast_kernels:
+ if not is_fast_path_available or "cuda" not in self.x_proj.weight.device.type:
+ raise ValueError(
+ "Fast Mamba kernels are not available. Make sure to they are installed and that the mamba module is on a CUDA device"
+ )
+ return self.cuda_kernels_forward(hidden_states, cache_params)
+ return self.slow_forward(hidden_states, cache_params)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor]]]:
+ if past_key_value is not None:
+ cache_params = MambaCacheParams(
+ seqlen_offset=0 if hidden_states.shape[1] > 1 else past_key_value.seen_tokens,
+ )
+ if len(past_key_value.key_cache) > self.layer_idx:
+ # we already have cache for this layer, use it
+ # remove the dummy seqlen dim (dim=2)
+ cache_params.conv_states[self.layer_idx] = past_key_value.key_cache[self.layer_idx].squeeze(2)
+ cache_params.ssm_states[self.layer_idx] = past_key_value.value_cache[self.layer_idx].squeeze(2)
+ else:
+ # we don't have cache for this layer, initialize it with zeros
+ batch_size = hidden_states.shape[0]
+ cache_params.conv_states[self.layer_idx] = torch.zeros(
+ batch_size,
+ self.intermediate_size,
+ self.conv_kernel_size,
+ device=hidden_states.device,
+ dtype=hidden_states.dtype,
+ )
+ cache_params.ssm_states[self.layer_idx] = torch.zeros(
+ batch_size,
+ self.intermediate_size,
+ self.ssm_state_size,
+ device=hidden_states.device,
+ dtype=hidden_states.dtype,
+ )
+ else:
+ cache_params = None
+
+ res = self.mixer_forward(hidden_states, cache_params)
+
+ if past_key_value is not None:
+ past_key_value.update(
+ # add dummy seqlen dim (dim=2) to match the number of dimensions of the attention cache
+ cache_params.conv_states[self.layer_idx].unsqueeze(2),
+ cache_params.ssm_states[self.layer_idx].unsqueeze(2),
+ self.layer_idx,
+ )
+
+ return res, past_key_value
+
+
+class JambaMLP(nn.Module):
+ def __init__(self, config: JambaConfig):
+ super().__init__()
+ self.ffn_dim = config.intermediate_size
+ self.hidden_dim = config.hidden_size
+
+ self.gate_proj = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+ self.down_proj = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False)
+ self.up_proj = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False)
+
+ self.act_fn = ACT2FN[config.hidden_act]
+
+ def forward(self, x):
+ return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+# Adapted from transformers.models.mixtral.modeling_mixtral.MixtralSparseMoeBlock with Mistral->Jamba
+class JambaSparseMoeBlock(nn.Module):
+ """
+ This implementation is
+ strictly equivalent to standard MoE with full capacity (no
+ dropped tokens). It's faster since it formulates MoE operations
+ in terms of block-sparse operations to accomodate imbalanced
+ assignments of tokens to experts, whereas standard MoE either
+ (1) drop tokens at the cost of reduced performance or (2) set
+ capacity factor to number of experts and thus waste computation
+ and memory on padding.
+ """
+
+ def __init__(self, config: JambaConfig, num_experts: int, num_experts_per_tok: int):
+ super().__init__()
+ self.hidden_dim = config.hidden_size
+ self.ffn_dim = config.intermediate_size
+
+ # these values are decided on runtime depending on the layer index
+ self.num_experts = num_experts
+ self.top_k = num_experts_per_tok
+
+ if num_experts > 1:
+ # expert routing
+ self.router = nn.Linear(self.hidden_dim, self.num_experts, bias=False)
+ else:
+ self.router = None
+
+ self.experts = nn.ModuleList([JambaMLP(config) for _ in range(self.num_experts)])
+
+ def forward(self, hidden_states: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+ """ """
+ batch_size, sequence_length, hidden_dim = hidden_states.shape
+
+ if self.num_experts == 1:
+ # in this case we have a single MLP block and don't need to do any routing
+ final_hidden_states = self.experts[0](hidden_states)
+ router_logits = torch.ones(
+ (batch_size * sequence_length, 1),
+ device=hidden_states.device,
+ dtype=hidden_states.dtype,
+ requires_grad=hidden_states.requires_grad,
+ )
+ return final_hidden_states, router_logits
+
+ # in this case we have multiple experts and need to do routing
+ hidden_states = hidden_states.view(-1, hidden_dim)
+ # router_logits: (batch * sequence_length, n_experts)
+ router_logits = self.router(hidden_states)
+ routing_weights = F.softmax(router_logits, dim=1, dtype=torch.float)
+ routing_weights, selected_experts = torch.topk(routing_weights, self.top_k, dim=-1)
+ # we cast back to the input dtype
+ routing_weights = routing_weights.to(hidden_states.dtype)
+
+ final_hidden_states = torch.zeros(
+ (batch_size * sequence_length, hidden_dim), dtype=hidden_states.dtype, device=hidden_states.device
+ )
+
+ # One hot encode the selected experts to create an expert mask
+ # this will be used to easily index which expert is going to be sollicitated
+ expert_mask = torch.nn.functional.one_hot(selected_experts, num_classes=self.num_experts).permute(2, 1, 0)
+
+ # Loop over all available experts in the model and perform the computation on each expert
+ for expert_idx in range(self.num_experts):
+ expert_layer = self.experts[expert_idx]
+ idx, top_x = torch.where(expert_mask[expert_idx])
+
+ if top_x.shape[0] == 0:
+ continue
+
+ # in torch it is faster to index using lists than torch tensors
+ top_x_list = top_x.tolist()
+ idx_list = idx.tolist()
+
+ # Index the correct hidden states and compute the expert hidden state for
+ # the current expert. We need to make sure to multiply the output hidden
+ # states by `routing_weights` on the corresponding tokens (top-1 and top-2)
+ current_state = hidden_states[None, top_x_list].reshape(-1, hidden_dim)
+ current_hidden_states = expert_layer(current_state) * routing_weights[top_x_list, idx_list, None]
+
+ # However `index_add_` only support torch tensors for indexing so we'll use
+ # the `top_x` tensor here.
+ final_hidden_states.index_add_(0, top_x, current_hidden_states.to(hidden_states.dtype))
+ final_hidden_states = final_hidden_states.reshape(batch_size, sequence_length, hidden_dim)
+ return final_hidden_states, router_logits
+
+
+class JambaAttentionDecoderLayer(nn.Module):
+ def __init__(self, config: JambaConfig, num_experts: int, layer_idx: int):
+ super().__init__()
+
+ self.self_attn = JAMBA_ATTENTION_CLASSES[config._attn_implementation](config, layer_idx)
+
+ num_experts_per_tok = config.num_experts_per_tok if num_experts > 1 else 1
+ self.moe = JambaSparseMoeBlock(config, num_experts=num_experts, num_experts_per_tok=num_experts_per_tok)
+ self.input_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.pre_moe_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[Tuple[torch.Tensor]] = None,
+ output_attentions: Optional[bool] = False,
+ output_router_logits: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ **kwargs,
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+ `(batch, sequence_length)` where padding elements are indicated by 0.
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_router_logits (`bool`, *optional*):
+ Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+ should not be returned during inference.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ """
+
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ hidden_states, self_attn_weights, present_key_value = self.self_attn(
+ hidden_states=hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_value,
+ output_attentions=output_attentions,
+ use_cache=use_cache,
+ )
+
+ # residual connection after attention
+ hidden_states = residual + hidden_states
+
+ # Experts
+ residual = hidden_states
+ hidden_states = self.pre_moe_layernorm(hidden_states)
+ hidden_states, router_logits = self.moe(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if use_cache:
+ outputs += (present_key_value,)
+
+ if output_router_logits:
+ outputs += (router_logits,)
+
+ return outputs
+
+
+class JambaMambaDecoderLayer(nn.Module):
+ def __init__(self, config: JambaConfig, num_experts: int, layer_idx: int):
+ super().__init__()
+
+ self.mamba = JambaMambaMixer(config=config, layer_idx=layer_idx)
+
+ num_experts_per_tok = config.num_experts_per_tok if num_experts > 1 else 1
+ self.moe = JambaSparseMoeBlock(config, num_experts=num_experts, num_experts_per_tok=num_experts_per_tok)
+ self.input_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+ self.pre_moe_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ def forward(
+ self,
+ hidden_states: torch.Tensor,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_value: Optional[HybridMambaAttentionDynamicCache] = None,
+ output_attentions: Optional[bool] = False,
+ output_router_logits: Optional[bool] = False,
+ use_cache: Optional[bool] = False,
+ **kwargs,
+ ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+ if "padding_mask" in kwargs:
+ warnings.warn(
+ "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+ )
+ """
+ Args:
+ hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+ attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
+ `(batch, sequence_length)` where padding elements are indicated by 0.
+ past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+ returned tensors for more detail.
+ output_router_logits (`bool`, *optional*):
+ Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+ should not be returned during inference.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
+ (see `past_key_values`).
+ """
+
+ residual = hidden_states
+
+ hidden_states = self.input_layernorm(hidden_states)
+
+ hidden_states, present_key_value = self.mamba(
+ hidden_states=hidden_states,
+ past_key_value=past_key_value,
+ )
+ bs, seqlen, _ = hidden_states.shape
+ past_seqlen = self._get_past_seqlen(past_key_value, seqlen)
+ num_attention_heads = self.mamba.config.num_attention_heads
+ self_attn_weights = torch.empty(bs, num_attention_heads, seqlen, past_seqlen, device="meta")
+
+ # residual connection after mamba
+ hidden_states = residual + hidden_states
+
+ # Experts
+ residual = hidden_states
+ hidden_states = self.pre_moe_layernorm(hidden_states)
+ hidden_states, router_logits = self.moe(hidden_states)
+ hidden_states = residual + hidden_states
+
+ outputs = (hidden_states,)
+
+ if output_attentions:
+ outputs += (self_attn_weights,)
+
+ if use_cache:
+ outputs += (present_key_value,)
+
+ if output_router_logits:
+ outputs += (router_logits,)
+
+ return outputs
+
+ def _get_past_seqlen(self, past_key_value, seqlen):
+ if past_key_value is None:
+ return seqlen
+ past_seqlen = past_key_value.get_seq_length()
+ if past_seqlen == 0:
+ return seqlen
+ if past_key_value.attention_layer_idx is None:
+ return seqlen
+ if self.mamba.layer_idx < past_key_value.attention_layer_idx:
+ return past_seqlen + 1
+ return past_seqlen
+
+
+JAMBA_START_DOCSTRING = r"""
+ This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
+ library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
+ etc.)
+
+ This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
+ Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
+ and behavior.
+
+ Parameters:
+ config ([`JambaConfig`]):
+ Model configuration class with all the parameters of the model. Initializing with a config file does not
+ load the weights associated with the model, only the configuration. Check out the
+ [`~PreTrainedModel.from_pretrained`] method to load the model weights.
+"""
+
+
+@add_start_docstrings(
+ "The bare Jamba Model outputting raw hidden-states without any specific head on top.",
+ JAMBA_START_DOCSTRING,
+)
+# Adapted from transformers.models.mistral.modeling_mistral.MistralPreTrainedModel with Mistral->Jamba
+class JambaPreTrainedModel(PreTrainedModel):
+ config_class = JambaConfig
+ base_model_prefix = "model"
+ supports_gradient_checkpointing = True
+ _no_split_modules = ["JambaAttentionDecoderLayer", "JambaMambaDecoderLayer"]
+ _skip_keys_device_placement = "past_key_values"
+ _supports_flash_attn_2 = True
+ _supports_sdpa = True
+ _supports_cache_class = True
+
+ def _init_weights(self, module):
+ std = self.config.initializer_range
+ if isinstance(module, (nn.Linear, nn.Conv1d)):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.bias is not None:
+ module.bias.data.zero_()
+ elif isinstance(module, nn.Embedding):
+ module.weight.data.normal_(mean=0.0, std=std)
+ if module.padding_idx is not None:
+ module.weight.data[module.padding_idx].zero_()
+
+ @staticmethod
+ def _convert_to_standard_cache(
+ past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]], batch_size: int
+ ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
+ """
+ Standardizes the format of the cache so as to match most implementations, i.e. have the seqlen as the third dim
+ also for mamba layers
+ """
+ attn_layer_index = [k.shape == v.shape for k, v in past_key_value].index(True)
+ seqlen = past_key_value[attn_layer_index][0].shape[2]
+ standard_past_key_value = ()
+ for k, v in past_key_value:
+ if k.shape != v.shape:
+ # mamba layer
+ # expand doesn't use more memory, so it's fine to do it here
+ standard_past_key_value += ((k.expand(-1, -1, seqlen, -1), v.expand(-1, -1, seqlen, -1)),)
+ else:
+ standard_past_key_value += ((k, v),)
+ return standard_past_key_value
+
+ @staticmethod
+ def _convert_to_jamba_cache(
+ past_key_value: Tuple[Tuple[torch.Tensor, torch.Tensor]],
+ ) -> Tuple[Tuple[torch.Tensor, torch.Tensor]]:
+ """
+ Converts the cache to the format expected by Jamba, i.e. dummy seqlen dimesion with size 1 for mamba layers
+ """
+ jamba_past_key_value = ()
+ for k, v in past_key_value:
+ if k.shape != v.shape:
+ # mamba layer
+ jamba_past_key_value += ((k[:, :, :1, :], v[:, :, :1, :]),)
+ else:
+ jamba_past_key_value += ((k, v),)
+ return jamba_past_key_value
+
+
+JAMBA_INPUTS_DOCSTRING = r"""
+ Args:
+ input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+ Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+ it.
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ [What are input IDs?](../glossary#input-ids)
+ attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
+
+ - 1 for tokens that are **not masked**,
+ - 0 for tokens that are **masked**.
+
+ [What are attention masks?](../glossary#attention-mask)
+
+ Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+ [`PreTrainedTokenizer.__call__`] for details.
+
+ If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+ `past_key_values`).
+
+ If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+ and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+ information on the default strategy.
+
+ - 1 indicates the head is **not masked**,
+ - 0 indicates the head is **masked**.
+ position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
+ config.n_positions - 1]`.
+
+ [What are position IDs?](../glossary#position-ids)
+ past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
+ Tuple of `tuple(torch.FloatTensor)` of length `config.num_hidden_layers`, with each tuple having 2 tensors
+ corresponding to the cache of the layer.
+ For attention layers, both tensors have shape of `(batch_size, num_kv_heads, sequence_length, embed_size_per_head)`
+ For mamba layers, the first tensor represents the convolution state and has shape of `(batch_size, d_inner, 1, d_conv)`,
+ and the second tensor represents the ssm state and has shape of `(batch_size, d_inner, 1, d_state)`. Mamba
+ cache shape[2] is a dummy "seqlen" dimension to match the number of attention cache dimensions. For mamba,
+ the cache doesn't grow with seqlen so this dimension is always 1.
+
+ Contains pre-computed hidden-states (key and values in the self-attention blocks and convolution and
+ ssm states in the mamba blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
+
+ If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that
+ don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
+ `input_ids` of shape `(batch_size, sequence_length)`.
+ inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
+ Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
+ is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
+ model's internal embedding lookup matrix.
+ use_cache (`bool`, *optional*):
+ If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
+ `past_key_values`).
+ output_attentions (`bool`, *optional*):
+ Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
+ tensors for more detail.
+ output_hidden_states (`bool`, *optional*):
+ Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
+ more detail.
+ output_router_logits (`bool`, *optional*):
+ Whether or not to return the logits of all the routers. They are useful for computing the router loss, and
+ should not be returned during inference.
+ return_dict (`bool`, *optional*):
+ Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
+"""
+
+
+@add_start_docstrings(
+ "The bare Jamba Model outputting raw hidden-states without any specific head on top.",
+ JAMBA_START_DOCSTRING,
+)
+# Adapted from transformers.models.mistral.modeling_mistral.MistralModel with MISTRAL->JAMBA, Mistral->Jamba
+class JambaModel(JambaPreTrainedModel):
+ """
+ Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`JambaDecoderLayer`]
+
+ Args:
+ config: JambaConfig
+ """
+
+ def __init__(self, config: JambaConfig):
+ super().__init__(config)
+ self.padding_idx = config.pad_token_id
+ self.vocab_size = config.vocab_size
+
+ self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+
+ # init each model layer, decide if it's mamba/attention and has experts or not
+ decoder_layers = []
+ for i in range(config.num_hidden_layers):
+ is_attn = True if (i - self.config.attn_layer_offset) % self.config.attn_layer_period == 0 else False
+ is_expert = True if (i - self.config.expert_layer_offset) % self.config.expert_layer_period == 0 else False
+
+ num_experts = self.config.num_experts if is_expert else 1
+ if is_attn:
+ decoder_layers.append(JambaAttentionDecoderLayer(config, num_experts=num_experts, layer_idx=i))
+ else:
+ decoder_layers.append(JambaMambaDecoderLayer(config, num_experts=num_experts, layer_idx=i))
+
+ if not any(isinstance(layer, JambaAttentionDecoderLayer) for layer in decoder_layers):
+ raise ValueError("At least one layer in the decoder must be an attention layer")
+ self._attn_layer_index = [isinstance(layer, JambaAttentionDecoderLayer) for layer in decoder_layers].index(
+ True
+ )
+
+ if not any(isinstance(layer, JambaMambaDecoderLayer) for layer in decoder_layers):
+ raise ValueError("At least one layer in the decoder must be a Mamba layer")
+ self._mamba_layer_index = [isinstance(layer, JambaMambaDecoderLayer) for layer in decoder_layers].index(True)
+
+ if (
+ decoder_layers[self._mamba_layer_index].mamba.ssm_state_size
+ == decoder_layers[self._mamba_layer_index].mamba.conv_kernel_size
+ ):
+ raise ValueError("Mamba state size and convolution size must be different")
+
+ self.layers = nn.ModuleList(decoder_layers)
+
+ self._attn_implementation = config._attn_implementation
+ self.final_layernorm = JambaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+ self.gradient_checkpointing = False
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.embed_tokens = value
+
+ # Ignore copy
+ @add_start_docstrings_to_model_forward(JAMBA_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[Union[List[torch.FloatTensor], HybridMambaAttentionDynamicCache]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, MoeModelOutputWithPast]:
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_router_logits = (
+ output_router_logits if output_router_logits is not None else self.config.output_router_logits
+ )
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ use_cache = use_cache if use_cache is not None else self.config.use_cache
+
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # retrieve input_ids and inputs_embeds
+ if input_ids is not None and inputs_embeds is not None:
+ raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+ elif input_ids is not None:
+ batch_size, seq_length = input_ids.shape
+ elif inputs_embeds is not None:
+ batch_size, seq_length, _ = inputs_embeds.shape
+ else:
+ raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+ past_key_values_length = 0
+
+ if self.gradient_checkpointing and self.training:
+ if use_cache:
+ logger.warning_once(
+ "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
+ )
+ use_cache = False
+
+ if use_cache:
+ if isinstance(past_key_values, Cache) and not isinstance(
+ past_key_values, HybridMambaAttentionDynamicCache
+ ):
+ past_key_values = HybridMambaAttentionDynamicCache.from_legacy_cache(past_key_values.to_legacy_cache())
+ use_legacy_cache = not isinstance(past_key_values, HybridMambaAttentionDynamicCache)
+ if use_legacy_cache:
+ past_key_values = HybridMambaAttentionDynamicCache.from_legacy_cache(past_key_values)
+ past_key_values_length = past_key_values.get_usable_length(seq_length, self._attn_layer_index)
+
+ if position_ids is None:
+ device = input_ids.device if input_ids is not None else inputs_embeds.device
+ position_ids = torch.arange(
+ past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+ )
+ position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
+ else:
+ position_ids = position_ids.view(-1, seq_length).long()
+
+ if inputs_embeds is None:
+ inputs_embeds = self.embed_tokens(input_ids)
+
+ if attention_mask is not None and self._attn_implementation == "flash_attention_2" and use_cache:
+ is_padding_right = attention_mask[:, -1].sum().item() != batch_size
+ if is_padding_right:
+ raise ValueError(
+ "You are attempting to perform batched generation with padding_side='right'"
+ " this may lead to unexpected behaviour for Flash Attention version of Jamba. Make sure to "
+ " call `tokenizer.padding_side = 'left'` before tokenizing the input. "
+ )
+
+ if self._attn_implementation == "flash_attention_2":
+ # 2d mask is passed through the layers
+ attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+ elif self._attn_implementation == "sdpa" and not output_attentions:
+ # output_attentions=True can not be supported when using SDPA, and we fall back on
+ # the manual implementation that requires a 4D causal mask in all cases.
+ attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+ attention_mask,
+ (batch_size, seq_length),
+ inputs_embeds,
+ past_key_values_length,
+ )
+ else:
+ # 4d mask is passed through the layers
+ attention_mask = _prepare_4d_causal_attention_mask(
+ attention_mask,
+ (batch_size, seq_length),
+ inputs_embeds,
+ past_key_values_length,
+ sliding_window=self.config.sliding_window,
+ )
+
+ hidden_states = inputs_embeds
+
+ # decoder layers
+ all_hidden_states = () if output_hidden_states else None
+ all_self_attns = () if output_attentions else None
+ all_router_logits = () if output_router_logits else None
+ next_decoder_cache = None
+
+ for decoder_layer in self.layers:
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ if self.gradient_checkpointing and self.training:
+ layer_outputs = self._gradient_checkpointing_func(
+ decoder_layer.__call__,
+ hidden_states,
+ attention_mask,
+ position_ids,
+ past_key_values,
+ output_attentions,
+ output_router_logits,
+ use_cache,
+ )
+ else:
+ layer_outputs = decoder_layer(
+ hidden_states,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_value=past_key_values,
+ output_attentions=output_attentions,
+ output_router_logits=output_router_logits,
+ use_cache=use_cache,
+ )
+
+ hidden_states = layer_outputs[0]
+
+ if use_cache:
+ next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+ if output_attentions:
+ all_self_attns += (layer_outputs[1],)
+
+ if output_router_logits:
+ all_router_logits += (layer_outputs[-1],)
+
+ hidden_states = self.final_layernorm(hidden_states)
+
+ # add hidden states from the last decoder layer
+ if output_hidden_states:
+ all_hidden_states += (hidden_states,)
+
+ next_cache = None
+ if use_cache:
+ next_cache = next_decoder_cache.to_legacy_cache() if use_legacy_cache else next_decoder_cache
+
+ if not return_dict:
+ return tuple(
+ v
+ for v in [hidden_states, next_cache, all_hidden_states, all_self_attns, all_router_logits]
+ if v is not None
+ )
+ return MoeModelOutputWithPast(
+ last_hidden_state=hidden_states,
+ past_key_values=next_cache,
+ hidden_states=all_hidden_states,
+ attentions=all_self_attns,
+ router_logits=all_router_logits,
+ )
+
+
+# Adapted from transformers.models.mixtral.modeling_mixtral.MixtralForCausalLM with MIXTRAL->JAMBA, Mixtral->Jamba
+class JambaForCausalLM(JambaPreTrainedModel):
+ _tied_weights_keys = ["lm_head.weight"]
+
+ def __init__(self, config: JambaConfig):
+ super().__init__(config)
+ self.model = JambaModel(config)
+ self.vocab_size = config.vocab_size
+ self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+ self.router_aux_loss_coef = config.router_aux_loss_coef
+ self.num_experts = config.num_experts
+ self.num_experts_per_tok = config.num_experts_per_tok
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ def get_output_embeddings(self):
+ return self.lm_head
+
+ def set_output_embeddings(self, new_embeddings):
+ self.lm_head = new_embeddings
+
+ def set_decoder(self, decoder):
+ self.model = decoder
+
+ def get_decoder(self):
+ return self.model
+
+ @add_start_docstrings_to_model_forward(JAMBA_INPUTS_DOCSTRING)
+ @replace_return_docstrings(output_type=MoeCausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+ # Ignore copy
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ output_router_logits: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ calc_logits_for_entire_prompt: Optional[bool] = True,
+ ) -> Union[Tuple, MoeCausalLMOutputWithPast]:
+ r"""
+ Args:
+ labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+ Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+ config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+ (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+ calc_logits_for_entire_prompt (`bool`, *optional*):
+ Whether or not to calculate the logits for the entire prompt, or just the last token. Only last token
+ logits are needed for generation, and calculating them only for that token can save memory,
+ which becomes pretty significant for long sequences.
+
+ Returns:
+ ```"""
+
+ output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+ output_router_logits = (
+ output_router_logits if output_router_logits is not None else self.config.output_router_logits
+ )
+
+ output_hidden_states = (
+ output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+ )
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+ outputs = self.model(
+ input_ids=input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ output_router_logits=output_router_logits,
+ return_dict=return_dict,
+ )
+
+ hidden_states = outputs[0]
+ if calc_logits_for_entire_prompt:
+ logits = self.lm_head(hidden_states)
+ else:
+ logits = self.lm_head(hidden_states[..., -1:, :])
+ logits = logits.float()
+
+ loss = None
+ if labels is not None:
+ # Shift so that tokens < n predict n
+ shift_logits = logits[..., :-1, :].contiguous()
+ shift_labels = labels[..., 1:].contiguous()
+ # Flatten the tokens
+ loss_fct = CrossEntropyLoss()
+ shift_logits = shift_logits.view(-1, self.config.vocab_size)
+ shift_labels = shift_labels.view(-1)
+ # Enable model parallelism
+ shift_labels = shift_labels.to(shift_logits.device)
+ loss = loss_fct(shift_logits, shift_labels)
+
+ aux_loss = None
+ if output_router_logits:
+ aux_loss = load_balancing_loss_func(
+ outputs.router_logits if return_dict else outputs[-1],
+ self.num_experts,
+ self.num_experts_per_tok,
+ attention_mask,
+ )
+ if labels is not None:
+ loss += self.router_aux_loss_coef * aux_loss.to(loss.device) # make sure to reside in the same device
+
+ if not return_dict:
+ output = (logits,) + outputs[1:]
+ if output_router_logits:
+ output = (aux_loss,) + output
+ return (loss,) + output if loss is not None else output
+
+ return MoeCausalLMOutputWithPast(
+ loss=loss,
+ aux_loss=aux_loss,
+ logits=logits,
+ past_key_values=outputs.past_key_values,
+ hidden_states=outputs.hidden_states,
+ attentions=outputs.attentions,
+ router_logits=outputs.router_logits,
+ )
+
+ def prepare_inputs_for_generation(
+ self,
+ input_ids,
+ past_key_values=None,
+ attention_mask=None,
+ inputs_embeds=None,
+ output_router_logits=False,
+ **kwargs,
+ ):
+ # Omit tokens covered by past_key_values
+ if past_key_values is not None:
+ # the cache may be in the stardard format (e.g. in contrastive search), convert to Jamba's format if needed
+ if isinstance(past_key_values, Tuple):
+ if past_key_values[self.model._mamba_layer_index][0].shape[2] > 1:
+ past_key_values = self._convert_to_jamba_cache(past_key_values)
+
+ if isinstance(past_key_values, Cache):
+ if not isinstance(past_key_values, HybridMambaAttentionDynamicCache):
+ past_key_values = HybridMambaAttentionDynamicCache.from_legacy_cache(
+ past_key_values.to_legacy_cache()
+ )
+ cache_length = past_key_values.get_seq_length()
+ past_length = past_key_values.seen_tokens
+ max_cache_length = past_key_values.get_max_length()
+ else:
+ cache_length = past_length = past_key_values[self.model._attn_layer_index][0].shape[2]
+ max_cache_length = None
+
+ # Keep only the unprocessed tokens:
+ # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+ # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as
+ # input)
+ if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+ input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+ # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+ # input_ids based on the past_length.
+ elif past_length < input_ids.shape[1]:
+ input_ids = input_ids[:, past_length:]
+ # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+ # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+ if (
+ max_cache_length is not None
+ and attention_mask is not None
+ and cache_length + input_ids.shape[1] > max_cache_length
+ ):
+ attention_mask = attention_mask[:, -max_cache_length:]
+
+ position_ids = kwargs.get("position_ids", None)
+ if attention_mask is not None and position_ids is None:
+ # create position_ids on the fly for batch generation
+ position_ids = attention_mask.long().cumsum(-1) - 1
+ position_ids.masked_fill_(attention_mask == 0, 1)
+ if past_key_values:
+ position_ids = position_ids[:, -input_ids.shape[1] :]
+
+ # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+ if inputs_embeds is not None and past_key_values is None:
+ model_inputs = {"inputs_embeds": inputs_embeds}
+ else:
+ model_inputs = {"input_ids": input_ids}
+
+ model_inputs.update(
+ {
+ "position_ids": position_ids,
+ "past_key_values": past_key_values,
+ "use_cache": kwargs.get("use_cache"),
+ "attention_mask": attention_mask,
+ "output_router_logits": output_router_logits,
+ "calc_logits_for_entire_prompt": self.config.calc_logits_for_entire_prompt,
+ }
+ )
+ return model_inputs
+
+ @staticmethod
+ def _reorder_cache(past_key_values, beam_idx):
+ reordered_past = ()
+ for layer_past in past_key_values:
+ reordered_past += (
+ tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+ )
+ return reordered_past
+
+
+@add_start_docstrings(
+ """
+ The Jamba Model with a sequence classification head on top (linear layer).
+
+ [`JambaForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+ (e.g. GPT-2) do.
+
+ Since it does classification on the last token, it requires to know the position of the last token. If a
+ `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
+ no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
+ padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
+ each row of the batch).
+ """,
+ JAMBA_START_DOCSTRING,
+)
+# Copied from transformers.models.mixtral.modeling_mixtral.MixtralForSequenceClassification with Mixtral->Jamba, MIXTRAL->JAMBA
+class JambaForSequenceClassification(JambaPreTrainedModel):
+ def __init__(self, config):
+ super().__init__(config)
+ self.num_labels = config.num_labels
+ self.model = JambaModel(config)
+ self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+ # Initialize weights and apply final processing
+ self.post_init()
+
+ def get_input_embeddings(self):
+ return self.model.embed_tokens
+
+ def set_input_embeddings(self, value):
+ self.model.embed_tokens = value
+
+ @add_start_docstrings_to_model_forward(JAMBA_INPUTS_DOCSTRING)
+ def forward(
+ self,
+ input_ids: torch.LongTensor = None,
+ attention_mask: Optional[torch.Tensor] = None,
+ position_ids: Optional[torch.LongTensor] = None,
+ past_key_values: Optional[List[torch.FloatTensor]] = None,
+ inputs_embeds: Optional[torch.FloatTensor] = None,
+ labels: Optional[torch.LongTensor] = None,
+ use_cache: Optional[bool] = None,
+ output_attentions: Optional[bool] = None,
+ output_hidden_states: Optional[bool] = None,
+ return_dict: Optional[bool] = None,
+ ) -> Union[Tuple, SequenceClassifierOutputWithPast]:
+ r"""
+ labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+ Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
+ config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+ `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+ """
+ return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+ transformer_outputs = self.model(
+ input_ids,
+ attention_mask=attention_mask,
+ position_ids=position_ids,
+ past_key_values=past_key_values,
+ inputs_embeds=inputs_embeds,
+ use_cache=use_cache,
+ output_attentions=output_attentions,
+ output_hidden_states=output_hidden_states,
+ return_dict=return_dict,
+ )
+ hidden_states = transformer_outputs[0]
+ logits = self.score(hidden_states)
+
+ if input_ids is not None:
+ batch_size = input_ids.shape[0]
+ else:
+ batch_size = inputs_embeds.shape[0]
+
+ if self.config.pad_token_id is None and batch_size != 1:
+ raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
+ if self.config.pad_token_id is None:
+ sequence_lengths = -1
+ else:
+ if input_ids is not None:
+ # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility
+ sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1
+ sequence_lengths = sequence_lengths % input_ids.shape[-1]
+ sequence_lengths = sequence_lengths.to(logits.device)
+ else:
+ sequence_lengths = -1
+
+ pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
+
+ loss = None
+ if labels is not None:
+ labels = labels.to(logits.device)
+ if self.config.problem_type is None:
+ if self.num_labels == 1:
+ self.config.problem_type = "regression"
+ elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+ self.config.problem_type = "single_label_classification"
+ else:
+ self.config.problem_type = "multi_label_classification"
+
+ if self.config.problem_type == "regression":
+ loss_fct = MSELoss()
+ if self.num_labels == 1:
+ loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
+ else:
+ loss = loss_fct(pooled_logits, labels)
+ elif self.config.problem_type == "single_label_classification":
+ loss_fct = CrossEntropyLoss()
+ loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
+ elif self.config.problem_type == "multi_label_classification":
+ loss_fct = BCEWithLogitsLoss()
+ loss = loss_fct(pooled_logits, labels)
+ if not return_dict:
+ output = (pooled_logits,) + transformer_outputs[1:]
+ return ((loss,) + output) if loss is not None else output
+
+ return SequenceClassifierOutputWithPast(
+ loss=loss,
+ logits=pooled_logits,
+ past_key_values=transformer_outputs.past_key_values,
+ hidden_states=transformer_outputs.hidden_states,
+ attentions=transformer_outputs.attentions,
+ )